Several model-agnostic methods exist for estimating predictive uncertainty.
[1]} proposed a `Trust Score', which is a simple method to estimate the trustworthiness of individual predictions made by a classifier.
The method estimates the trustworthiness of a prediction by measuring the agreement between a classifier and a modified nearest-neighbor classifier on a particular test instance.
If an instance is classified into a very different class than similar instances, there is reason to believe that the prediction might be faulty.
The intuition behind this is similar to the main idea behind case-based reasoning (CBR) methods, which assume that similar problems should have similar solutions [2]}, [3]}, [4]}.
